Maxwell A. Sherman

This study identifies somatic copy number variants (sCNVs) in schizophrenia cases, revealing their contribution to risk and specific NRXN1 disruptions.

The paper identifies protein-altering structural variants in UK Biobank data and finds associations with various quantitative traits, including hypertension and type 2 diabetes risk.

DeepMosaic detects mosaic single nucleotide variants without control samples using deep convolutional neural networks, achieving high accuracy and uncovering many variants missed by existing methods.

The paper identifies non-coding VNTRs associated with glaucoma and colorectal cancer risk using UK Biobank data and statistical imputation.

This paper develops a computational method to detect rare CNVs in UK Biobank data, identifies 269 CNV-trait associations, and highlights novel gene-trait relationships.

This study maps RNA translation dynamics in the human brain using ribosome profiling, identifying thousands of novel translation events and brain-specific microproteins linked to schizophrenia.

The paper analyzes recessive Mendelian disease variants in UK Biobank to find mitigated phenotypes in heterozygous carriers, identifying associations with quantitative and disease traits.

The paper evaluates brain somatic mosaicism in the adult human to identify rules governing cellular distribution during embryogenesis.

The paper estimates VNTR lengths from whole-exome sequencing data and identifies associations between VNTRs and human phenotypes, finding strong effects on traits like height and lipoprotein(a).

The paper uses whole-exome imputation in UK Biobank to identify rare coding variant associations and fine-mapping results for 54 traits, finding 1,189 significant associations involving 675 novel variants.

This study identifies somatic SNVs in non-cancerous human brain tissues and establishes best practices for detecting low-frequency variants.

The paper investigates the role of mosaic copy number variants (mCNVs) in autism spectrum disorder (ASD) by analyzing genetic data from ASD probands and unaffected siblings, finding a significant association between large mCNVs and ASD risk.

The paper introduces a machine learning method to detect somatic L1 insertions in human brain tissue, revealing their widespread distribution in neurons and glia and potential role in neuropsychiatric diseases.

The paper analyzes somatic mutations in autistic and control brains, finding higher mutation rates in ASD brains, particularly in enhancer regions.

The paper introduces LiRA, a method to distinguish true mutations from artifacts in single-cell DNA sequencing data by phasing somatic variants with germline variants.

The paper identifies age-related accumulation of somatic mutations in human neurons and links them to neurodegenerative diseases.

PaSD-qc evaluates scWGS library quality using power spectral density to assess amplification uniformity and identify aberrant chromosomes.